some new features
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ilgazca
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import math
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import numpy as np
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from scipy import special
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from scipy.stats._qmc import primes_from_2_to
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def _primes(n):
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# Defined to facilitate comparison between translation and source
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# In Matlab, primes(10.5) -> first four primes, primes(11.5) -> first five
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return primes_from_2_to(math.ceil(n))
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def _gaminv(a, b):
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# Defined to facilitate comparison between translation and source
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# Matlab's `gaminv` is like `special.gammaincinv` but args are reversed
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return special.gammaincinv(b, a)
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def _qsimvtv(m, nu, sigma, a, b, rng):
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"""Estimates the multivariate t CDF using randomized QMC
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Parameters
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----------
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m : int
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The number of points
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nu : float
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Degrees of freedom
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sigma : ndarray
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A 2D positive semidefinite covariance matrix
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a : ndarray
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Lower integration limits
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b : ndarray
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Upper integration limits.
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rng : Generator
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Pseudorandom number generator
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Returns
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-------
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p : float
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The estimated CDF.
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e : float
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An absolute error estimate.
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"""
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# _qsimvtv is a Python translation of the Matlab function qsimvtv,
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# semicolons and all.
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#
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# This function uses an algorithm given in the paper
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# "Comparison of Methods for the Numerical Computation of
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# Multivariate t Probabilities", in
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# J. of Computational and Graphical Stat., 11(2002), pp. 950-971, by
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# Alan Genz and Frank Bretz
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#
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# The primary references for the numerical integration are
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# "On a Number-Theoretical Integration Method"
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# H. Niederreiter, Aequationes Mathematicae, 8(1972), pp. 304-11.
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# and
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# "Randomization of Number Theoretic Methods for Multiple Integration"
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# R. Cranley & T.N.L. Patterson, SIAM J Numer Anal, 13(1976), pp. 904-14.
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#
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# Alan Genz is the author of this function and following Matlab functions.
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# Alan Genz, WSU Math, PO Box 643113, Pullman, WA 99164-3113
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# Email : alangenz@wsu.edu
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#
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# Copyright (C) 2013, Alan Genz, All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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# 2. Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in
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# the documentation and/or other materials provided with the
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# distribution.
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# 3. The contributor name(s) may not be used to endorse or promote
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# products derived from this software without specific prior
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# written permission.
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
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# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
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# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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# Initialization
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sn = max(1, math.sqrt(nu)); ch, az, bz = _chlrps(sigma, a/sn, b/sn)
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n = len(sigma); N = 10; P = math.ceil(m/N); on = np.ones(P); p = 0; e = 0
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ps = np.sqrt(_primes(5*n*math.log(n+4)/4)); q = ps[:, np.newaxis] # Richtmyer gens.
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# Randomization loop for ns samples
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c = None; dc = None
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for S in range(N):
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vp = on.copy(); s = np.zeros((n, P))
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for i in range(n):
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x = np.abs(2*np.mod(q[i]*np.arange(1, P+1) + rng.random(), 1)-1) # periodizing transform
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if i == 0:
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r = on
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if nu > 0:
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r = np.sqrt(2*_gaminv(x, nu/2))
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else:
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y = _Phinv(c + x*dc)
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s[i:] += ch[i:, i-1:i] * y
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si = s[i, :]; c = on.copy(); ai = az[i]*r - si; d = on.copy(); bi = bz[i]*r - si
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c[ai <= -9] = 0; tl = abs(ai) < 9; c[tl] = _Phi(ai[tl])
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d[bi <= -9] = 0; tl = abs(bi) < 9; d[tl] = _Phi(bi[tl])
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dc = d - c; vp = vp * dc
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d = (np.mean(vp) - p)/(S + 1); p = p + d; e = (S - 1)*e/(S + 1) + d**2
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e = math.sqrt(e) # error estimate is 3 times std error with N samples.
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return p, e
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# Standard statistical normal distribution functions
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def _Phi(z):
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return special.ndtr(z)
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def _Phinv(p):
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return special.ndtri(p)
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def _chlrps(R, a, b):
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"""
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Computes permuted and scaled lower Cholesky factor c for R which may be
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singular, also permuting and scaling integration limit vectors a and b.
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"""
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ep = 1e-10 # singularity tolerance
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eps = np.finfo(R.dtype).eps
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n = len(R); c = R.copy(); ap = a.copy(); bp = b.copy(); d = np.sqrt(np.maximum(np.diag(c), 0))
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for i in range(n):
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if d[i] > 0:
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c[:, i] /= d[i]; c[i, :] /= d[i]
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ap[i] /= d[i]; bp[i] /= d[i]
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y = np.zeros((n, 1)); sqtp = math.sqrt(2*math.pi)
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for k in range(n):
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im = k; ckk = 0; dem = 1; s = 0
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for i in range(k, n):
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if c[i, i] > eps:
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cii = math.sqrt(max(c[i, i], 0))
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if i > 0: s = c[i, :k] @ y[:k]
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ai = (ap[i]-s)/cii; bi = (bp[i]-s)/cii; de = _Phi(bi)-_Phi(ai)
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if de <= dem:
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ckk = cii; dem = de; am = ai; bm = bi; im = i
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if im > k:
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ap[[im, k]] = ap[[k, im]]; bp[[im, k]] = bp[[k, im]]; c[im, im] = c[k, k]
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t = c[im, :k].copy(); c[im, :k] = c[k, :k]; c[k, :k] = t
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t = c[im+1:, im].copy(); c[im+1:, im] = c[im+1:, k]; c[im+1:, k] = t
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t = c[k+1:im, k].copy(); c[k+1:im, k] = c[im, k+1:im].T; c[im, k+1:im] = t.T
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if ckk > ep*(k+1):
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c[k, k] = ckk; c[k, k+1:] = 0
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for i in range(k+1, n):
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c[i, k] = c[i, k]/ckk; c[i, k+1:i+1] = c[i, k+1:i+1] - c[i, k]*c[k+1:i+1, k].T
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if abs(dem) > ep:
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y[k] = (np.exp(-am**2/2) - np.exp(-bm**2/2)) / (sqtp*dem)
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else:
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y[k] = (am + bm) / 2
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if am < -10:
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y[k] = bm
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elif bm > 10:
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y[k] = am
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c[k, :k+1] /= ckk; ap[k] /= ckk; bp[k] /= ckk
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else:
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c[k:, k] = 0; y[k] = (ap[k] + bp[k])/2
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pass
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return c, ap, bp
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@ -0,0 +1,607 @@
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# DO NOT EDIT THIS FILE!
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# This file was generated by the R script
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# generate_fisher_exact_results_from_r.R
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# The script was run with R version 3.6.2 (2019-12-12) at 2020-11-09 06:16:09
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from collections import namedtuple
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import numpy as np
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Inf = np.inf
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Parameters = namedtuple('Parameters',
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['table', 'confidence_level', 'alternative'])
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RResults = namedtuple('RResults',
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['pvalue', 'conditional_odds_ratio',
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'conditional_odds_ratio_ci'])
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data = [
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(Parameters(table=[[100, 2], [1000, 5]],
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confidence_level=0.95,
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alternative='two.sided'),
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RResults(pvalue=0.1300759363430016,
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conditional_odds_ratio=0.25055839934223,
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conditional_odds_ratio_ci=(0.04035202926536294,
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2.662846672960251))),
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(Parameters(table=[[2, 7], [8, 2]],
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confidence_level=0.95,
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alternative='two.sided'),
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RResults(pvalue=0.02301413756522116,
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conditional_odds_ratio=0.0858623513573622,
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conditional_odds_ratio_ci=(0.004668988338943325,
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0.895792956493601))),
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(Parameters(table=[[5, 1], [10, 10]],
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confidence_level=0.95,
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alternative='two.sided'),
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RResults(pvalue=0.1973244147157191,
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conditional_odds_ratio=4.725646047336587,
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conditional_odds_ratio_ci=(0.4153910882532168,
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259.2593661129417))),
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(Parameters(table=[[5, 15], [20, 20]],
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confidence_level=0.95,
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alternative='two.sided'),
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RResults(pvalue=0.09580440012477633,
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conditional_odds_ratio=0.3394396617440851,
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conditional_odds_ratio_ci=(0.08056337526385809,
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1.22704788545557))),
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(Parameters(table=[[5, 16], [16, 25]],
|
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confidence_level=0.95,
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alternative='two.sided'),
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RResults(pvalue=0.2697004098849359,
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conditional_odds_ratio=0.4937791394540491,
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conditional_odds_ratio_ci=(0.1176691231650079,
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1.787463657995973))),
|
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(Parameters(table=[[10, 5], [10, 1]],
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confidence_level=0.95,
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alternative='two.sided'),
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RResults(pvalue=0.1973244147157192,
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||||
conditional_odds_ratio=0.2116112781158479,
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||||
conditional_odds_ratio_ci=(0.003857141267422399,
|
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2.407369893767229))),
|
||||
(Parameters(table=[[10, 5], [10, 0]],
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||||
confidence_level=0.95,
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alternative='two.sided'),
|
||||
RResults(pvalue=0.06126482213438735,
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||||
conditional_odds_ratio=0,
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||||
conditional_odds_ratio_ci=(0,
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||||
1.451643573543705))),
|
||||
(Parameters(table=[[5, 0], [1, 4]],
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||||
confidence_level=0.95,
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alternative='two.sided'),
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||||
RResults(pvalue=0.04761904761904762,
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||||
conditional_odds_ratio=Inf,
|
||||
conditional_odds_ratio_ci=(1.024822256141754,
|
||||
Inf))),
|
||||
(Parameters(table=[[0, 5], [1, 4]],
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||||
confidence_level=0.95,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
39.00054996869288))),
|
||||
(Parameters(table=[[5, 1], [0, 4]],
|
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confidence_level=0.95,
|
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alternative='two.sided'),
|
||||
RResults(pvalue=0.04761904761904761,
|
||||
conditional_odds_ratio=Inf,
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conditional_odds_ratio_ci=(1.024822256141754,
|
||||
Inf))),
|
||||
(Parameters(table=[[0, 1], [3, 2]],
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||||
confidence_level=0.95,
|
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alternative='two.sided'),
|
||||
RResults(pvalue=1,
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||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
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||||
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|
||||
(Parameters(table=[[200, 7], [8, 300]],
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||||
confidence_level=0.95,
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alternative='two.sided'),
|
||||
RResults(pvalue=2.005657880389071e-122,
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||||
conditional_odds_ratio=977.7866978606228,
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||||
conditional_odds_ratio_ci=(349.2595113327733,
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||||
3630.382605689872))),
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||||
(Parameters(table=[[28, 21], [6, 1957]],
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||||
confidence_level=0.95,
|
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alternative='two.sided'),
|
||||
RResults(pvalue=5.728437460831947e-44,
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||||
conditional_odds_ratio=425.2403028434684,
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||||
conditional_odds_ratio_ci=(152.4166024390096,
|
||||
1425.700792178893))),
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||||
(Parameters(table=[[190, 800], [200, 900]],
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||||
confidence_level=0.95,
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||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.574111858126088,
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conditional_odds_ratio=1.068697577856801,
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conditional_odds_ratio_ci=(0.8520462587912048,
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||||
1.340148950273938))),
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(Parameters(table=[[100, 2], [1000, 5]],
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||||
confidence_level=0.99,
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||||
alternative='two.sided'),
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||||
RResults(pvalue=0.1300759363430016,
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conditional_odds_ratio=0.25055839934223,
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conditional_odds_ratio_ci=(0.02502345007115455,
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(Parameters(table=[[2, 7], [8, 2]],
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confidence_level=0.99,
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alternative='two.sided'),
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RResults(pvalue=0.02301413756522116,
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conditional_odds_ratio=0.0858623513573622,
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conditional_odds_ratio_ci=(0.001923034001462487,
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||||
1.53670836950172))),
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(Parameters(table=[[5, 1], [10, 10]],
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||||
confidence_level=0.99,
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||||
alternative='two.sided'),
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||||
RResults(pvalue=0.1973244147157191,
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||||
conditional_odds_ratio=4.725646047336587,
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conditional_odds_ratio_ci=(0.2397970951413721,
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||||
1291.342011095509))),
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(Parameters(table=[[5, 15], [20, 20]],
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||||
confidence_level=0.99,
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||||
alternative='two.sided'),
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||||
RResults(pvalue=0.09580440012477633,
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||||
conditional_odds_ratio=0.3394396617440851,
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||||
conditional_odds_ratio_ci=(0.05127576113762925,
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||||
1.717176678806983))),
|
||||
(Parameters(table=[[5, 16], [16, 25]],
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||||
confidence_level=0.99,
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||||
alternative='two.sided'),
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||||
RResults(pvalue=0.2697004098849359,
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||||
conditional_odds_ratio=0.4937791394540491,
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||||
conditional_odds_ratio_ci=(0.07498546954483619,
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||||
2.506969905199901))),
|
||||
(Parameters(table=[[10, 5], [10, 1]],
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||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.1973244147157192,
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||||
conditional_odds_ratio=0.2116112781158479,
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||||
conditional_odds_ratio_ci=(0.0007743881879531337,
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||||
4.170192301163831))),
|
||||
(Parameters(table=[[10, 5], [10, 0]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.06126482213438735,
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||||
conditional_odds_ratio=0,
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||||
conditional_odds_ratio_ci=(0,
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||||
2.642491011905582))),
|
||||
(Parameters(table=[[5, 0], [1, 4]],
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||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.04761904761904762,
|
||||
conditional_odds_ratio=Inf,
|
||||
conditional_odds_ratio_ci=(0.496935393325443,
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||||
Inf))),
|
||||
(Parameters(table=[[0, 5], [1, 4]],
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||||
confidence_level=0.99,
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||||
alternative='two.sided'),
|
||||
RResults(pvalue=1,
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||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
198.019801980198))),
|
||||
(Parameters(table=[[5, 1], [0, 4]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.04761904761904761,
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||||
conditional_odds_ratio=Inf,
|
||||
conditional_odds_ratio_ci=(0.496935393325443,
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||||
Inf))),
|
||||
(Parameters(table=[[0, 1], [3, 2]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=1,
|
||||
conditional_odds_ratio=0,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
198.019801980198))),
|
||||
(Parameters(table=[[200, 7], [8, 300]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=2.005657880389071e-122,
|
||||
conditional_odds_ratio=977.7866978606228,
|
||||
conditional_odds_ratio_ci=(270.0334165523604,
|
||||
5461.333333326708))),
|
||||
(Parameters(table=[[28, 21], [6, 1957]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=5.728437460831947e-44,
|
||||
conditional_odds_ratio=425.2403028434684,
|
||||
conditional_odds_ratio_ci=(116.7944750275836,
|
||||
1931.995993191814))),
|
||||
(Parameters(table=[[190, 800], [200, 900]],
|
||||
confidence_level=0.99,
|
||||
alternative='two.sided'),
|
||||
RResults(pvalue=0.574111858126088,
|
||||
conditional_odds_ratio=1.068697577856801,
|
||||
conditional_odds_ratio_ci=(0.7949398282935892,
|
||||
1.436229679394333))),
|
||||
(Parameters(table=[[100, 2], [1000, 5]],
|
||||
confidence_level=0.95,
|
||||
alternative='less'),
|
||||
RResults(pvalue=0.1300759363430016,
|
||||
conditional_odds_ratio=0.25055839934223,
|
||||
conditional_odds_ratio_ci=(0,
|
||||
1.797867027270803))),
|
||||
(Parameters(table=[[2, 7], [8, 2]],
|
||||
confidence_level=0.95,
|
||||
alternative='less'),
|
||||
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||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@ -0,0 +1,108 @@
|
||||
NIST/ITL StRD
|
||||
Dataset Name: AtmWtAg (AtmWtAg.dat)
|
||||
|
||||
|
||||
File Format: ASCII
|
||||
Certified Values (lines 41 to 47)
|
||||
Data (lines 61 to 108)
|
||||
|
||||
|
||||
Procedure: Analysis of Variance
|
||||
|
||||
|
||||
Reference: Powell, L.J., Murphy, T.J. and Gramlich, J.W. (1982).
|
||||
"The Absolute Isotopic Abundance & Atomic Weight
|
||||
of a Reference Sample of Silver".
|
||||
NBS Journal of Research, 87, pp. 9-19.
|
||||
|
||||
|
||||
Data: 1 Factor
|
||||
2 Treatments
|
||||
24 Replicates/Cell
|
||||
48 Observations
|
||||
7 Constant Leading Digits
|
||||
Average Level of Difficulty
|
||||
Observed Data
|
||||
|
||||
|
||||
Model: 3 Parameters (mu, tau_1, tau_2)
|
||||
y_{ij} = mu + tau_i + epsilon_{ij}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Certified Values:
|
||||
|
||||
Source of Sums of Mean
|
||||
Variation df Squares Squares F Statistic
|
||||
|
||||
|
||||
Between Instrument 1 3.63834187500000E-09 3.63834187500000E-09 1.59467335677930E+01
|
||||
Within Instrument 46 1.04951729166667E-08 2.28155932971014E-10
|
||||
|
||||
Certified R-Squared 2.57426544538321E-01
|
||||
|
||||
Certified Residual
|
||||
Standard Deviation 1.51048314446410E-05
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Data: Instrument AgWt
|
||||
1 107.8681568
|
||||
1 107.8681465
|
||||
1 107.8681572
|
||||
1 107.8681785
|
||||
1 107.8681446
|
||||
1 107.8681903
|
||||
1 107.8681526
|
||||
1 107.8681494
|
||||
1 107.8681616
|
||||
1 107.8681587
|
||||
1 107.8681519
|
||||
1 107.8681486
|
||||
1 107.8681419
|
||||
1 107.8681569
|
||||
1 107.8681508
|
||||
1 107.8681672
|
||||
1 107.8681385
|
||||
1 107.8681518
|
||||
1 107.8681662
|
||||
1 107.8681424
|
||||
1 107.8681360
|
||||
1 107.8681333
|
||||
1 107.8681610
|
||||
1 107.8681477
|
||||
2 107.8681079
|
||||
2 107.8681344
|
||||
2 107.8681513
|
||||
2 107.8681197
|
||||
2 107.8681604
|
||||
2 107.8681385
|
||||
2 107.8681642
|
||||
2 107.8681365
|
||||
2 107.8681151
|
||||
2 107.8681082
|
||||
2 107.8681517
|
||||
2 107.8681448
|
||||
2 107.8681198
|
||||
2 107.8681482
|
||||
2 107.8681334
|
||||
2 107.8681609
|
||||
2 107.8681101
|
||||
2 107.8681512
|
||||
2 107.8681469
|
||||
2 107.8681360
|
||||
2 107.8681254
|
||||
2 107.8681261
|
||||
2 107.8681450
|
||||
2 107.8681368
|
||||
@ -0,0 +1,85 @@
|
||||
NIST/ITL StRD
|
||||
Dataset Name: SiRstv (SiRstv.dat)
|
||||
|
||||
|
||||
File Format: ASCII
|
||||
Certified Values (lines 41 to 47)
|
||||
Data (lines 61 to 85)
|
||||
|
||||
|
||||
Procedure: Analysis of Variance
|
||||
|
||||
|
||||
Reference: Ehrstein, James and Croarkin, M. Carroll.
|
||||
Unpublished NIST dataset.
|
||||
|
||||
|
||||
Data: 1 Factor
|
||||
5 Treatments
|
||||
5 Replicates/Cell
|
||||
25 Observations
|
||||
3 Constant Leading Digits
|
||||
Lower Level of Difficulty
|
||||
Observed Data
|
||||
|
||||
|
||||
Model: 6 Parameters (mu,tau_1, ... , tau_5)
|
||||
y_{ij} = mu + tau_i + epsilon_{ij}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Certified Values:
|
||||
|
||||
Source of Sums of Mean
|
||||
Variation df Squares Squares F Statistic
|
||||
|
||||
Between Instrument 4 5.11462616000000E-02 1.27865654000000E-02 1.18046237440255E+00
|
||||
Within Instrument 20 2.16636560000000E-01 1.08318280000000E-02
|
||||
|
||||
Certified R-Squared 1.90999039051129E-01
|
||||
|
||||
Certified Residual
|
||||
Standard Deviation 1.04076068334656E-01
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Data: Instrument Resistance
|
||||
1 196.3052
|
||||
1 196.1240
|
||||
1 196.1890
|
||||
1 196.2569
|
||||
1 196.3403
|
||||
2 196.3042
|
||||
2 196.3825
|
||||
2 196.1669
|
||||
2 196.3257
|
||||
2 196.0422
|
||||
3 196.1303
|
||||
3 196.2005
|
||||
3 196.2889
|
||||
3 196.0343
|
||||
3 196.1811
|
||||
4 196.2795
|
||||
4 196.1748
|
||||
4 196.1494
|
||||
4 196.1485
|
||||
4 195.9885
|
||||
5 196.2119
|
||||
5 196.1051
|
||||
5 196.1850
|
||||
5 196.0052
|
||||
5 196.2090
|
||||
@ -0,0 +1,249 @@
|
||||
NIST/ITL StRD
|
||||
Dataset Name: SmLs01 (SmLs01.dat)
|
||||
|
||||
|
||||
File Format: ASCII
|
||||
Certified Values (lines 41 to 47)
|
||||
Data (lines 61 to 249)
|
||||
|
||||
|
||||
Procedure: Analysis of Variance
|
||||
|
||||
|
||||
Reference: Simon, Stephen D. and Lesage, James P. (1989).
|
||||
"Assessing the Accuracy of ANOVA Calculations in
|
||||
Statistical Software".
|
||||
Computational Statistics & Data Analysis, 8, pp. 325-332.
|
||||
|
||||
|
||||
Data: 1 Factor
|
||||
9 Treatments
|
||||
21 Replicates/Cell
|
||||
189 Observations
|
||||
1 Constant Leading Digit
|
||||
Lower Level of Difficulty
|
||||
Generated Data
|
||||
|
||||
|
||||
Model: 10 Parameters (mu,tau_1, ... , tau_9)
|
||||
y_{ij} = mu + tau_i + epsilon_{ij}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Certified Values:
|
||||
|
||||
Source of Sums of Mean
|
||||
Variation df Squares Squares F Statistic
|
||||
|
||||
Between Treatment 8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01
|
||||
Within Treatment 180 1.80000000000000E+00 1.00000000000000E-02
|
||||
|
||||
Certified R-Squared 4.82758620689655E-01
|
||||
|
||||
Certified Residual
|
||||
Standard Deviation 1.00000000000000E-01
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Data: Treatment Response
|
||||
1 1.4
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
1 1.3
|
||||
1 1.5
|
||||
2 1.3
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
2 1.2
|
||||
2 1.4
|
||||
3 1.5
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
3 1.4
|
||||
3 1.6
|
||||
4 1.3
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
4 1.2
|
||||
4 1.4
|
||||
5 1.5
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
5 1.4
|
||||
5 1.6
|
||||
6 1.3
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
6 1.2
|
||||
6 1.4
|
||||
7 1.5
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
7 1.4
|
||||
7 1.6
|
||||
8 1.3
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
8 1.2
|
||||
8 1.4
|
||||
9 1.5
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
9 1.4
|
||||
9 1.6
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,249 @@
|
||||
NIST/ITL StRD
|
||||
Dataset Name: SmLs04 (SmLs04.dat)
|
||||
|
||||
|
||||
File Format: ASCII
|
||||
Certified Values (lines 41 to 47)
|
||||
Data (lines 61 to 249)
|
||||
|
||||
|
||||
Procedure: Analysis of Variance
|
||||
|
||||
|
||||
Reference: Simon, Stephen D. and Lesage, James P. (1989).
|
||||
"Assessing the Accuracy of ANOVA Calculations in
|
||||
Statistical Software".
|
||||
Computational Statistics & Data Analysis, 8, pp. 325-332.
|
||||
|
||||
|
||||
Data: 1 Factor
|
||||
9 Treatments
|
||||
21 Replicates/Cell
|
||||
189 Observations
|
||||
7 Constant Leading Digits
|
||||
Average Level of Difficulty
|
||||
Generated Data
|
||||
|
||||
|
||||
Model: 10 Parameters (mu,tau_1, ... , tau_9)
|
||||
y_{ij} = mu + tau_i + epsilon_{ij}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Certified Values:
|
||||
|
||||
Source of Sums of Mean
|
||||
Variation df Squares Squares F Statistic
|
||||
|
||||
Between Treatment 8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01
|
||||
Within Treatment 180 1.80000000000000E+00 1.00000000000000E-02
|
||||
|
||||
Certified R-Squared 4.82758620689655E-01
|
||||
|
||||
Certified Residual
|
||||
Standard Deviation 1.00000000000000E-01
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Data: Treatment Response
|
||||
1 1000000.4
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
1 1000000.3
|
||||
1 1000000.5
|
||||
2 1000000.3
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
2 1000000.2
|
||||
2 1000000.4
|
||||
3 1000000.5
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
3 1000000.4
|
||||
3 1000000.6
|
||||
4 1000000.3
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
4 1000000.2
|
||||
4 1000000.4
|
||||
5 1000000.5
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
5 1000000.4
|
||||
5 1000000.6
|
||||
6 1000000.3
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
6 1000000.2
|
||||
6 1000000.4
|
||||
7 1000000.5
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
7 1000000.4
|
||||
7 1000000.6
|
||||
8 1000000.3
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
8 1000000.2
|
||||
8 1000000.4
|
||||
9 1000000.5
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
9 1000000.4
|
||||
9 1000000.6
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,249 @@
|
||||
NIST/ITL StRD
|
||||
Dataset Name: SmLs07 (SmLs07.dat)
|
||||
|
||||
|
||||
File Format: ASCII
|
||||
Certified Values (lines 41 to 47)
|
||||
Data (lines 61 to 249)
|
||||
|
||||
|
||||
Procedure: Analysis of Variance
|
||||
|
||||
|
||||
Reference: Simon, Stephen D. and Lesage, James P. (1989).
|
||||
"Assessing the Accuracy of ANOVA Calculations in
|
||||
Statistical Software".
|
||||
Computational Statistics & Data Analysis, 8, pp. 325-332.
|
||||
|
||||
|
||||
Data: 1 Factor
|
||||
9 Treatments
|
||||
21 Replicates/Cell
|
||||
189 Observations
|
||||
13 Constant Leading Digits
|
||||
Higher Level of Difficulty
|
||||
Generated Data
|
||||
|
||||
|
||||
Model: 10 Parameters (mu,tau_1, ... , tau_9)
|
||||
y_{ij} = mu + tau_i + epsilon_{ij}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Certified Values:
|
||||
|
||||
Source of Sums of Mean
|
||||
Variation df Squares Squares F Statistic
|
||||
|
||||
Between Treatment 8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01
|
||||
Within Treatment 180 1.80000000000000E+00 1.00000000000000E-02
|
||||
|
||||
Certified R-Squared 4.82758620689655E-01
|
||||
|
||||
Certified Residual
|
||||
Standard Deviation 1.00000000000000E-01
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Data: Treatment Response
|
||||
1 1000000000000.4
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
1 1000000000000.3
|
||||
1 1000000000000.5
|
||||
2 1000000000000.3
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
2 1000000000000.2
|
||||
2 1000000000000.4
|
||||
3 1000000000000.5
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
3 1000000000000.4
|
||||
3 1000000000000.6
|
||||
4 1000000000000.3
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
4 1000000000000.2
|
||||
4 1000000000000.4
|
||||
5 1000000000000.5
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
5 1000000000000.4
|
||||
5 1000000000000.6
|
||||
6 1000000000000.3
|
||||
6 1000000000000.2
|
||||
6 1000000000000.4
|
||||
6 1000000000000.2
|
||||
6 1000000000000.4
|
||||
6 1000000000000.2
|
||||
6 1000000000000.4
|
||||
6 1000000000000.2
|
||||
6 1000000000000.4
|
||||
6 1000000000000.2
|
||||
6 1000000000000.4
|
||||
6 1000000000000.2
|
||||
6 1000000000000.4
|
||||
6 1000000000000.2
|
||||
6 1000000000000.4
|
||||
6 1000000000000.2
|
||||
6 1000000000000.4
|
||||
6 1000000000000.2
|
||||
6 1000000000000.4
|
||||
6 1000000000000.2
|
||||
6 1000000000000.4
|
||||
7 1000000000000.5
|
||||
7 1000000000000.4
|
||||
7 1000000000000.6
|
||||
7 1000000000000.4
|
||||
7 1000000000000.6
|
||||
7 1000000000000.4
|
||||
7 1000000000000.6
|
||||
7 1000000000000.4
|
||||
7 1000000000000.6
|
||||
7 1000000000000.4
|
||||
7 1000000000000.6
|
||||
7 1000000000000.4
|
||||
7 1000000000000.6
|
||||
7 1000000000000.4
|
||||
7 1000000000000.6
|
||||
7 1000000000000.4
|
||||
7 1000000000000.6
|
||||
7 1000000000000.4
|
||||
7 1000000000000.6
|
||||
7 1000000000000.4
|
||||
7 1000000000000.6
|
||||
8 1000000000000.3
|
||||
8 1000000000000.2
|
||||
8 1000000000000.4
|
||||
8 1000000000000.2
|
||||
8 1000000000000.4
|
||||
8 1000000000000.2
|
||||
8 1000000000000.4
|
||||
8 1000000000000.2
|
||||
8 1000000000000.4
|
||||
8 1000000000000.2
|
||||
8 1000000000000.4
|
||||
8 1000000000000.2
|
||||
8 1000000000000.4
|
||||
8 1000000000000.2
|
||||
8 1000000000000.4
|
||||
8 1000000000000.2
|
||||
8 1000000000000.4
|
||||
8 1000000000000.2
|
||||
8 1000000000000.4
|
||||
8 1000000000000.2
|
||||
8 1000000000000.4
|
||||
9 1000000000000.5
|
||||
9 1000000000000.4
|
||||
9 1000000000000.6
|
||||
9 1000000000000.4
|
||||
9 1000000000000.6
|
||||
9 1000000000000.4
|
||||
9 1000000000000.6
|
||||
9 1000000000000.4
|
||||
9 1000000000000.6
|
||||
9 1000000000000.4
|
||||
9 1000000000000.6
|
||||
9 1000000000000.4
|
||||
9 1000000000000.6
|
||||
9 1000000000000.4
|
||||
9 1000000000000.6
|
||||
9 1000000000000.4
|
||||
9 1000000000000.6
|
||||
9 1000000000000.4
|
||||
9 1000000000000.6
|
||||
9 1000000000000.4
|
||||
9 1000000000000.6
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,97 @@
|
||||
NIST/ITL StRD
|
||||
Dataset Name: Norris (Norris.dat)
|
||||
|
||||
File Format: ASCII
|
||||
Certified Values (lines 31 to 46)
|
||||
Data (lines 61 to 96)
|
||||
|
||||
Procedure: Linear Least Squares Regression
|
||||
|
||||
Reference: Norris, J., NIST.
|
||||
Calibration of Ozone Monitors.
|
||||
|
||||
Data: 1 Response Variable (y)
|
||||
1 Predictor Variable (x)
|
||||
36 Observations
|
||||
Lower Level of Difficulty
|
||||
Observed Data
|
||||
|
||||
Model: Linear Class
|
||||
2 Parameters (B0,B1)
|
||||
|
||||
y = B0 + B1*x + e
|
||||
|
||||
|
||||
|
||||
Certified Regression Statistics
|
||||
|
||||
Standard Deviation
|
||||
Parameter Estimate of Estimate
|
||||
|
||||
B0 -0.262323073774029 0.232818234301152
|
||||
B1 1.00211681802045 0.429796848199937E-03
|
||||
|
||||
Residual
|
||||
Standard Deviation 0.884796396144373
|
||||
|
||||
R-Squared 0.999993745883712
|
||||
|
||||
|
||||
Certified Analysis of Variance Table
|
||||
|
||||
Source of Degrees of Sums of Mean
|
||||
Variation Freedom Squares Squares F Statistic
|
||||
|
||||
Regression 1 4255954.13232369 4255954.13232369 5436385.54079785
|
||||
Residual 34 26.6173985294224 0.782864662630069
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Data: y x
|
||||
0.1 0.2
|
||||
338.8 337.4
|
||||
118.1 118.2
|
||||
888.0 884.6
|
||||
9.2 10.1
|
||||
228.1 226.5
|
||||
668.5 666.3
|
||||
998.5 996.3
|
||||
449.1 448.6
|
||||
778.9 777.0
|
||||
559.2 558.2
|
||||
0.3 0.4
|
||||
0.1 0.6
|
||||
778.1 775.5
|
||||
668.8 666.9
|
||||
339.3 338.0
|
||||
448.9 447.5
|
||||
10.8 11.6
|
||||
557.7 556.0
|
||||
228.3 228.1
|
||||
998.0 995.8
|
||||
888.8 887.6
|
||||
119.6 120.2
|
||||
0.3 0.3
|
||||
0.6 0.3
|
||||
557.6 556.8
|
||||
339.3 339.1
|
||||
888.0 887.2
|
||||
998.5 999.0
|
||||
778.9 779.0
|
||||
10.2 11.1
|
||||
117.6 118.3
|
||||
228.9 229.2
|
||||
668.4 669.1
|
||||
449.2 448.9
|
||||
0.2 0.5
|
||||
|
||||
Binary file not shown.
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user